Research and Application of Deep Learning Models with Multi-Scale Feature Fusion for Lesion Segmentation in Oral Mucosal Diseases

Given the complexity of oral mucosal disease diagnosis and the limitations in the precision of traditional object detection methods, this study aims to develop a high-accuracy artificial intelligence-assisted diagnostic approach based on the SegFormer semantic segmentation model. This method is designed to automatically segment lesion areas in white-light images of oral mucosal diseases, providing objective and quantifiable evidence for clinical diagnosis. This study utilized a dataset of oral mucosal diseases provided by the Affiliated Stomatological Hospital of Zhejiang University School of Medicine, comprising 838 high-resolution images of three diseases: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. These images were annotated at the pixel level by oral specialists using Labelme software (v5.5.0) to construct a semantic segmentation dataset. This study designed a SegFormer model based on the Transformer architecture, employed cross-validation to divide training and testing sets, and compared SegFormer models of different capacities with classical segmentation models such as UNet and DeepLabV3. Quantitative metrics including the Dice coefficient and mIoU were evaluated, and a qualitative visual analysis of the segmentation results was performed to comprehensively assess model performance. The SegFormer-B2 model achieved optimal performance on the test set, with a Dice coefficient of 0.710 and mIoU of 0.786, significantly outperforming other comparative algorithms. The visual results demonstrate that this model could accurately segment the lesion areas of three common oral mucosal diseases. The SegFormer model proposed in this study effectively achieves the precise automatic segmentation of three common oral mucosal diseases, providing a reliable auxiliary tool for clinical diagnosis. It shows promising prospects in improving the efficiency and accuracy of oral mucosal disease diagnosis and has potential clinical application value.